
import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


__lowerCAmelCase     :	Optional[Any]       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	List[str]       			=  {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase     :	int       			=  {
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
__lowerCAmelCase     :	Any       			=  {
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
__lowerCAmelCase     :	List[Any]       			=  {
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

__lowerCAmelCase     :	Any       			=  {
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
__lowerCAmelCase     :	Optional[int]       			=  {
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
__lowerCAmelCase     :	Optional[int]       			=  {
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


__lowerCAmelCase     :	List[str]       			=  {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
__lowerCAmelCase     :	str       			=  {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
__lowerCAmelCase     :	Dict       			=  {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						VOCAB_FILES_NAMES
  a__       			=						CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  a__       			=						CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a__       			=						CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
  a__       			=						DPRContextEncoderTokenizer







class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						VOCAB_FILES_NAMES
  a__       			=						QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  a__       			=						QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a__       			=						QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
  a__       			=						DPRQuestionEncoderTokenizer







__lowerCAmelCase     :	Optional[int]       			=  collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

__lowerCAmelCase     :	Union[str, Any]       			=  collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


__lowerCAmelCase     :	str       			=  R'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '




@add_start_docstrings(_A	)
class 			UpperCAmelCase_     :


  '''simple docstring'''







  def __call__(					self     :					Tuple     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					Optional[str] = None     ,				UpperCamelCase__     :					Optional[str] = None     ,				UpperCamelCase__     :					Union[bool, str] = False     ,				UpperCamelCase__     :					Union[bool, str] = False     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					Optional[Union[str, TensorType]] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				**UpperCamelCase__     :					List[str]     ,				)     ->      BatchEncoding:







    """simple docstring"""






    if titles is None and texts is None:
      return super().__call__(
          UpperCamelCase__     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				max_length=UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				return_attention_mask=UpperCamelCase__     ,				**UpperCamelCase__     ,				)
    elif titles is None or texts is None:
      __magic_name__								=			titles if texts is None else texts
      return super().__call__(
          UpperCamelCase__     ,				UpperCamelCase__     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				max_length=UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				return_attention_mask=UpperCamelCase__     ,				**UpperCamelCase__     ,				)
    __magic_name__								=			titles if not isinstance(UpperCamelCase__     ,				UpperCamelCase__       ) else [titles]
    __magic_name__								=			texts if not isinstance(UpperCamelCase__     ,				UpperCamelCase__       ) else [texts]
    __magic_name__								=			len(UpperCamelCase__       )
    __magic_name__								=			questions if not isinstance(UpperCamelCase__     ,				UpperCamelCase__       ) else [questions] * n_passages
    assert len(UpperCamelCase__       ) == len(
        UpperCamelCase__       ), F'''There should be as many titles than texts but got {len(UpperCamelCase__       )} titles and {len(UpperCamelCase__       )} texts.'''
    __magic_name__								=			super().__call__(UpperCamelCase__     ,				UpperCamelCase__     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__       )["""input_ids"""]
    __magic_name__								=			super().__call__(UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__       )["""input_ids"""]
    __magic_name__								=			{
        """input_ids""": [
            (encoded_question_and_title + encoded_text)[:max_length]
            if max_length is not None and truncation
            else encoded_question_and_title + encoded_text
            for encoded_question_and_title, encoded_text in zip(UpperCamelCase__     ,				UpperCamelCase__       )
        ]
    }
    if return_attention_mask is not False:
      __magic_name__								=			[]
      for input_ids in encoded_inputs["input_ids"]:
        attention_mask.append([int(input_id != self.pad_token_id       ) for input_id in input_ids]       )
      __magic_name__								=			attention_mask
    return self.pad(UpperCamelCase__     ,				padding=UpperCamelCase__     ,				max_length=UpperCamelCase__     ,				return_tensors=UpperCamelCase__       )
  def 				_lowercase				(					self     :					List[Any]     ,				UpperCamelCase__     :					BatchEncoding     ,				UpperCamelCase__     :					DPRReaderOutput     ,				UpperCamelCase__     :					int = 16     ,				UpperCamelCase__     :					int = 64     ,				UpperCamelCase__     :					int = 4     ,				)     ->      List[DPRSpanPrediction]:







    """simple docstring"""






    __magic_name__								=			reader_input["""input_ids"""]
    __magic_name__	,       __magic_name__	,       __magic_name__								=			reader_output[:3]
    __magic_name__								=			len(UpperCamelCase__       )
    __magic_name__								=			sorted(range(UpperCamelCase__       )     ,				reverse=UpperCamelCase__     ,				key=relevance_logits.__getitem__       )
    __magic_name__								=			[]
    for doc_id in sorted_docs:
      __magic_name__								=			list(input_ids[doc_id]       )
      # assuming question & title information is at the beginning of the sequence
      __magic_name__								=			sequence_ids.index(self.sep_token_id     ,				2       ) + 1  # second sep id
      if sequence_ids[-1] == self.pad_token_id:
        __magic_name__								=			sequence_ids.index(self.pad_token_id       )
      else:
        __magic_name__								=			len(UpperCamelCase__       )

      __magic_name__								=			self._get_best_spans(
          start_logits=start_logits[doc_id][passage_offset:sequence_len]     ,				end_logits=end_logits[doc_id][passage_offset:sequence_len]     ,				max_answer_length=UpperCamelCase__     ,				top_spans=UpperCamelCase__     ,				)
      for start_index, end_index in best_spans:
        start_index += passage_offset
        end_index += passage_offset
        nbest_spans_predictions.append(
            DPRSpanPrediction(
                span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]     ,				relevance_score=relevance_logits[doc_id]     ,				doc_id=UpperCamelCase__     ,				start_index=UpperCamelCase__     ,				end_index=UpperCamelCase__     ,				text=self.decode(sequence_ids[start_index : end_index + 1]       )     ,				)       )
      if len(UpperCamelCase__       ) >= num_spans:
        break
    return nbest_spans_predictions[:num_spans]





  def 				_lowercase				(					self     :					Optional[int]     ,				UpperCamelCase__     :					List[int]     ,				UpperCamelCase__     :					List[int]     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					int     ,				)     ->      List[DPRSpanPrediction]:







    """simple docstring"""






    __magic_name__								=			[]
    for start_index, start_score in enumerate(UpperCamelCase__       ):
      for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]       ):
        scores.append(((start_index, start_index + answer_length), start_score + end_score)       )
    __magic_name__								=			sorted(UpperCamelCase__     ,				key=lambda UpperCamelCase__       : x[1]     ,				reverse=UpperCamelCase__       )
    __magic_name__								=			[]
    for (start_index, end_index), score in scores:
      assert start_index <= end_index, F'''Wrong span indices: [{start_index}:{end_index}]'''
      __magic_name__								=			end_index - start_index + 1
      assert length <= max_answer_length, F'''Span is too long: {length} > {max_answer_length}'''
      if any(
          start_index <= prev_start_index <= prev_end_index <= end_index
          or prev_start_index <= start_index <= end_index <= prev_end_index
          for (prev_start_index, prev_end_index) in chosen_span_intervals       ):
        continue
      chosen_span_intervals.append((start_index, end_index)       )

      if len(UpperCamelCase__       ) == top_spans:
        break
    return chosen_span_intervals







@add_end_docstrings(_A	)
class 			UpperCAmelCase_     (					_A    ,			_A	):


  '''simple docstring'''







  a__       			=						VOCAB_FILES_NAMES
  a__       			=						READER_PRETRAINED_VOCAB_FILES_MAP
  a__       			=						READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a__       			=						READER_PRETRAINED_INIT_CONFIGURATION
  a__       			=						["""input_ids""", """attention_mask"""]
  a__       			=						DPRReaderTokenizer



import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__lowerCAmelCase     :	Optional[Any]       			=  {'LayoutLMv2Config', 'LayoutLMv3Config'}




@is_pipeline_test
class 			UpperCAmelCase_     (					unittest.TestCase	):


  '''simple docstring'''







  a__       			=						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
  a__       			=						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

  if model_mapping is not None:
    a__       			=						{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
  if tf_model_mapping is not None:
    a__       			=						{
        config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
    }
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					List[str]       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			ZeroShotClassificationPipeline(
        model=UpperCamelCase__     ,				tokenizer=UpperCamelCase__     ,				candidate_labels=["""polics""", """health"""]       )
    return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
  def 				_lowercase				(					self     :					int     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					List[Any]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			classifier("""Who are you voting for in 2020?"""     ,				candidate_labels="""politics"""       )
    self.assertEqual(UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       )]}       )

    # No kwarg
    __magic_name__								=			classifier("""Who are you voting for in 2020?"""     ,				["""politics"""]       )
    self.assertEqual(UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       )]}       )

    __magic_name__								=			classifier("""Who are you voting for in 2020?"""     ,				candidate_labels=["""politics"""]       )
    self.assertEqual(UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       )]}       )

    __magic_name__								=			classifier("""Who are you voting for in 2020?"""     ,				candidate_labels="""politics, public health"""       )
    self.assertEqual(
        UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )]}       )
    self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]       )       )     ,				1.0       )

    __magic_name__								=			classifier("""Who are you voting for in 2020?"""     ,				candidate_labels=["""politics""", """public health"""]       )
    self.assertEqual(
        UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )]}       )
    self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]       )       )     ,				1.0       )

    __magic_name__								=			classifier(
        """Who are you voting for in 2020?"""     ,				candidate_labels="""politics"""     ,				hypothesis_template="""This text is about {}"""       )
    self.assertEqual(UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       )]}       )

    # https://github.com/huggingface/transformers/issues/13846
    __magic_name__								=			classifier(["""I am happy"""]     ,				["""positive""", """negative"""]       )
    self.assertEqual(
        UpperCamelCase__     ,				[
            {"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )]}
            for i in range(1       )
        ]     ,				)
    __magic_name__								=			classifier(["""I am happy""", """I am sad"""]     ,				["""positive""", """negative"""]       )
    self.assertEqual(
        UpperCamelCase__     ,				[
            {"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )]}
            for i in range(2       )
        ]     ,				)

    with self.assertRaises(UpperCamelCase__       ):
      classifier(""""""     ,				candidate_labels="""politics"""       )

    with self.assertRaises(UpperCamelCase__       ):
      classifier(UpperCamelCase__     ,				candidate_labels="""politics"""       )

    with self.assertRaises(UpperCamelCase__       ):
      classifier("""Who are you voting for in 2020?"""     ,				candidate_labels=""""""       )

    with self.assertRaises(UpperCamelCase__       ):
      classifier("""Who are you voting for in 2020?"""     ,				candidate_labels=UpperCamelCase__       )

    with self.assertRaises(UpperCamelCase__       ):
      classifier(
          """Who are you voting for in 2020?"""     ,				candidate_labels="""politics"""     ,				hypothesis_template="""Not formatting template"""     ,				)

    with self.assertRaises(UpperCamelCase__       ):
      classifier(
          """Who are you voting for in 2020?"""     ,				candidate_labels="""politics"""     ,				hypothesis_template=UpperCamelCase__     ,				)

    self.run_entailment_id(UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					Pipeline       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			zero_shot_classifier.model.config
    __magic_name__								=			config.labelaid
    __magic_name__								=			zero_shot_classifier.entailment_id

    __magic_name__								=			{"""LABEL_0""": 0, """LABEL_1""": 1, """LABEL_2""": 2}
    self.assertEqual(zero_shot_classifier.entailment_id     ,				-1       )

    __magic_name__								=			{"""entailment""": 0, """neutral""": 1, """contradiction""": 2}
    self.assertEqual(zero_shot_classifier.entailment_id     ,				0       )

    __magic_name__								=			{"""ENTAIL""": 0, """NON-ENTAIL""": 1}
    self.assertEqual(zero_shot_classifier.entailment_id     ,				0       )

    __magic_name__								=			{"""ENTAIL""": 2, """NEUTRAL""": 1, """CONTR""": 0}
    self.assertEqual(zero_shot_classifier.entailment_id     ,				2       )

    __magic_name__								=			original_labelaid
    self.assertEqual(UpperCamelCase__     ,				zero_shot_classifier.entailment_id       )
  @require_torch
  def 				_lowercase				(					self     :					Optional[int]       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			pipeline(
        """zero-shot-classification"""     ,				model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""     ,				framework="""pt"""     ,				)
    # There was a regression in 4.10 for this
    # Adding a test so we don't make the mistake again.
    # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
    zero_shot_classifier(
        """Who are you voting for in 2020?""" * 100     ,				candidate_labels=["""politics""", """public health""", """science"""]       )
  @require_torch
  def 				_lowercase				(					self     :					Optional[int]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			pipeline(
        """zero-shot-classification"""     ,				model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""     ,				framework="""pt"""     ,				)
    __magic_name__								=			zero_shot_classifier(
        """Who are you voting for in 2020?"""     ,				candidate_labels=["""politics""", """public health""", """science"""]       )

    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": """Who are you voting for in 2020?""",
            """labels""": ["""science""", """public health""", """politics"""],
            """scores""": [0.333, 0.333, 0.333],
        }     ,				)
  @require_tf
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			pipeline(
        """zero-shot-classification"""     ,				model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""     ,				framework="""tf"""     ,				)
    __magic_name__								=			zero_shot_classifier(
        """Who are you voting for in 2020?"""     ,				candidate_labels=["""politics""", """public health""", """science"""]       )

    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": """Who are you voting for in 2020?""",
            """labels""": ["""science""", """public health""", """politics"""],
            """scores""": [0.333, 0.333, 0.333],
        }     ,				)
  @slow
  @require_torch
  def 				_lowercase				(					self     :					Any       )     ->      List[Any]:







    """simple docstring"""






    __magic_name__								=			pipeline("""zero-shot-classification"""     ,				model="""roberta-large-mnli"""     ,				framework="""pt"""       )
    __magic_name__								=			zero_shot_classifier(
        """Who are you voting for in 2020?"""     ,				candidate_labels=["""politics""", """public health""", """science"""]       )

    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": """Who are you voting for in 2020?""",
            """labels""": ["""politics""", """public health""", """science"""],
            """scores""": [0.976, 0.015, 0.009],
        }     ,				)
    __magic_name__								=			zero_shot_classifier(
        """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
        """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
        """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
        """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
        """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
        """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
        """ English-to-German translation task, improving over the existing best results, including ensembles by"""
        """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
        """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
        """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
        """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
        """ large and limited training data."""     ,				candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""]     ,				multi_label=UpperCamelCase__     ,				)
    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": (
                """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
                """ networks in an encoder-decoder configuration. The best performing models also connect the"""
                """ encoder and decoder through an attention mechanism. We propose a new simple network"""
                """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
                """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
                """ superior in quality while being more parallelizable and requiring significantly less time to"""
                """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
                """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
                """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
                """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
                """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
                """ other tasks by applying it successfully to English constituency parsing both with large and"""
                """ limited training data."""
            ),
            """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
            """scores""": [0.817, 0.713, 0.018, 0.018],
        }     ,				)





  @slow
  @require_tf
  def 				_lowercase				(					self     :					str       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			pipeline("""zero-shot-classification"""     ,				model="""roberta-large-mnli"""     ,				framework="""tf"""       )
    __magic_name__								=			zero_shot_classifier(
        """Who are you voting for in 2020?"""     ,				candidate_labels=["""politics""", """public health""", """science"""]       )

    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": """Who are you voting for in 2020?""",
            """labels""": ["""politics""", """public health""", """science"""],
            """scores""": [0.976, 0.015, 0.009],
        }     ,				)
    __magic_name__								=			zero_shot_classifier(
        """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
        """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
        """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
        """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
        """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
        """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
        """ English-to-German translation task, improving over the existing best results, including ensembles by"""
        """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
        """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
        """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
        """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
        """ large and limited training data."""     ,				candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""]     ,				multi_label=UpperCamelCase__     ,				)
    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": (
                """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
                """ networks in an encoder-decoder configuration. The best performing models also connect the"""
                """ encoder and decoder through an attention mechanism. We propose a new simple network"""
                """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
                """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
                """ superior in quality while being more parallelizable and requiring significantly less time to"""
                """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
                """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
                """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
                """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
                """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
                """ other tasks by applying it successfully to English constituency parsing both with large and"""
                """ limited training data."""
            ),
            """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
            """scores""": [0.817, 0.713, 0.018, 0.018],
        }     ,				)

